A liquid-phase Lagrangian parcel model was expanded to include nucleation and growth of ice crystals. Intercomparisons between three heterogeneous ice nucleation parameterizations that link aerosol type and number to ice crystal concentration were conducted. Results indicate large differences in the prediction of ice formation in modestly supercooled clouds and in the susceptibility of cirrus to heterogeneous ice nucleation for the same assumed aerosol distribution. Only one parameterization has an observational constraint that limits the maximum ice crystal number concentrations to be a fraction of the total number concentrations of potential ice-nucleating particles (typically, all insoluble particles larger than about 0.1 mm). The constrained parameterization compares well with most ice nucleation measurements. The nonconstrained parameterizations are capable of predicting several orders of magnitude higher ice crystal concentrations than the constrained parameterization for the same parcel-forcing conditions. Ice crystal concentrations in the unconstrained parameterizations are controlled by the total number concentration of potential ice-nucleating particles and, importantly, negative feedback on ice supersaturation. This feedback control often masks the large discrepancy that exists between predicted ice crystal number concentrations and the maximum number concentrations that can be attributed to atmospheric ice nuclei based on our current understanding of the latter. It also permits unrealistic conclusions regarding the role of certain aerosols as ice nuclei. It is recommended that a constraint on ice crystal number concentrations, related to number concentrations of relevant aerosol particles, should be included in ice nucleation parameterizations used in cloud to global-scale models.
A comparison of heterogeneous ice nucleation parameterizations using a parcel model framework
Eidhammer, T., P.J. DeMott, and S. Kreidenweis (2009), A comparison of heterogeneous ice nucleation parameterizations using a parcel model framework, J. Geophys. Res., 114, D06202, doi:10.1029/2008JD011095.
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Modeling Analysis and Prediction Program (MAP)